We tested whether a multisectoral household agricultural and finance intervention increased the dietary intake and improved the nutritional status of HIV-affected children. Two hospitals in rural Kenya were randomly assigned to be either the intervention or the control arm. The intervention comprised a human-powered water pump, microfinance loan for farm commodities, and training in sustainable farming practices and financial management. In each arm, 100 children (0-59 mo of age) were enrolled from households with HIV-infected adults 18-49 y old. Children were assessed beginning in April 2012 and every 3 mo for 1 y for dietary intake and anthropometry. Children in the intervention arm had a larger increase in weight (β: 0.025 kg/mo, P = 0.030), overall frequency of food consumption (β: 0.610 times · wk^-1 · mo^-1, P = 0.048), and intakes of staples (β: 0.222, P = 0.024), fruits and vegetables (β: 0.425, P = 0.005), meat (β: 0.074, P < 0.001), and fat (β: 0.057, P = 0.041). Livelihood interventions have potential to improve the nutrition of HIV-affected children. This trial was registered at clinicaltrials.gov as NCT01548599.

BACKGROUND:Preterm birth (birth before 37 weeks of gestation) and its complications are the leading contributors to neonatal and under-5 mortality. The majority of neonatal deaths in Kenya and Uganda occur during the intrapartum and immediate postnatal period. This paper describes our study protocol for implementing and evaluating a package of facility-based interventions to improve care during this critical window. METHODS/DESIGN:This is a pair-matched, cluster randomized controlled trial across 20 facilities in Eastern Uganda and Western Kenya. The intervention facilities receive four components: (1) strengthening of routine data collection and data use activities; (2) implementation of the WHO Safe Childbirth Checklist modified for preterm birth; (3) PRONTO simulation training and mentoring to strengthen intrapartum and immediate newborn care; and (4) support of quality improvement teams. The control facilities receive both data strengthening and introduction of the modified checklist. The primary outcome for this study is 28-day mortality rate among preterm infants. The denominator will include all live births and fresh stillbirths weighing greater than 1000 g and less than 2500 g; all live births and fresh stillbirths weighing between 2501 and 3000 g with a documented gestational age less than 37 weeks. DISCUSSION:The results of this study will inform interventions to improve personnel and facility capacity to respond to preterm labor and delivery, as well as care for the preterm infant. TRIAL REGISTRATION:ClinicalTrials.gov, ID: NCT03112018 . Registered on 13 April 2017.

Background

Quality of care during the intrapartum and immediate postnatal period for maternal and newborn health remains a major challenge due to the multiple health system bottlenecks in low-income countries. Reports of complex interventions that have been effective in reducing maternal and newborn mortality in these settings are usually limited in description, which inhibits learning and replication. We present a detailed account of the Preterm Birth Initiative (PTBi) implementation process, experiences and lessons learnt to inform scale-up and replication.

Methods

Using the TiDieR framework, we detail how the PTBi implemented an integrated package of interventions through a pair-matched cluster randomized control trial in 20 health facilities in Migori County, Kenya, and the Busoga region in east central Uganda from 2016 to 2019. The package aimed to improve quality of care during the intrapartum and immediate postnatal period with a focus on preterm birth. The package included data strengthening (DS) and introduction of a modified WHO Safe Childbirth Checklist (mSCC), simulation-based training and mentoring (PRONTO), and a Quality Improvement (QI) Collaborative.

Results

In 2016, DS and mSCC were introduced to improve existing data processes and increase the quality of data for measures needed to evaluate study impact. PRONTO and QI interventions were then rolled out sequentially. While package components were implemented with fidelity, some implementation processes required contextual adaptation to allow alignment with national priorities and guidelines, and flexibility to optimize uptake.

Conclusion

Lessons learned included the importance of synergy between interventions, the need for local leadership engagement, and the value of strengthening local systems and resources. Adaptations of individual elements of the package to suit the local context were important for effective implementation, and the TIDieR framework provides the guidance needed in detailed description to replicate such a complex intervention in other settings. Detailed documentation of the implementation process of a complex intervention with mutually synergistic components can help contextualize trial results and potential for scale-up. The trial is registered at ClinicalTrials.gov NCT03112018 , registered December 2016, posted April 2017.

Background

A large proportion of early neonatal deaths occur at the time or on the first day of birth. The Preterm Birth Initiative East Africa (PTBi EA) set out to decrease mortality among preterm births through improving quality of facility-based intrapartum care. The PTBi EA cluster randomized trial's primary analysis showed the package reduced intrapartum stillbirth and neonatal death among preterm infants. This secondary analysis examines the impact of the PTBi intervention package on stillbirth and predischarge newborn deaths combined, among all births in 20 participating facilities in Kenya and Uganda.

Methods

Eligible facilities were pair-matched and randomly assigned (1:1) into either the intervention or the control group. All facilities received support for data strengthening and a modified World Health Organization (WHO) Safe Childbirth Checklist; facilities in the intervention group additionally received provider mentoring using PRONTO simulation and team training as well as quality improvement collaboratives. We abstracted data from maternity registers.

Results

Of the total 29 442 births that were included, Kenya had 8468 and 6465 births and Uganda had 8719 and 5790 births, in the control and intervention arms, respectively. There were 935 stillbirths and predischarge newborn deaths in the control arm and 439 in the intervention arm. The adjusted odds ratio (aOR) for the effect of the intervention on the combined outcome, among all births, was 0.96 (95% confidence interval (CI) = 0.69-1.32), which was different by country: Kenya - 1.12 (95% CI = 0.72-1.73); Uganda - 0.65 (95% CI = 0.44-0.98); P_interaction  = 0.025. These trends were similar after excluding the PTBi primary cohort.

Conclusions

The intervention package improved survival among all births in Uganda but not in Kenya. These results suggest the importance of context and facility differences that were observed between the two countries.

Registration

This trial is registered with ClinicalTrials.gov, NCT03112018.

Objective

Preterm birth is the primary driver of neonatal mortality worldwide, but it is defined by gestational age (GA) which is challenging to accurately assess in low-resource settings. In a commitment to reducing preterm birth while reinforcing and strengthening facility data sources, the East Africa Preterm Birth Initiative (PTBi-EA) chose eligibility criteria that combined GA and birth weight. This analysis evaluated the quality of the GA data as recorded in maternity registers in PTBi-EA study facilities and the strength of the PTBi-EA eligibility criteria.

Methods

We conducted a retrospective analysis of maternity register data from March-September 2016. GA data from 23 study facilities in Migori, Kenya and the Busoga Region of Uganda were evaluated for completeness (variable present), consistency (recorded versus calculated GA), and plausibility (falling within the 3rd and 97th birth weight percentiles for GA of the INTERGROWTH-21st Newborn Birth Weight Standards). Preterm birth rates were calculated using: 1) recorded GA <37 weeks, 2) recorded GA <37 weeks, excluding implausible GAs, 3) birth weight <2500g, and 4) PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks.

Results

In both countries, GA was the least recorded variable in the maternity register (77.6%). Recorded and calculated GA (Kenya only) were consistent in 29.5% of births. Implausible GAs accounted for 11.7% of births. The four preterm birth rates were 1) 14.5%, 2) 10.6%, 3) 9.6%, 4) 13.4%.

Conclusions

Maternity register GA data presented quality concerns in PTBi-EA study sites. The PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks accommodated these concerns by using both birth weight and GA, balancing issues of accuracy and completeness with practical applicability.

Background

The WHO Safe Childbirth Checklist (SCC) contains 29 evidence-based practices (EBPs) across four pause points spanning admission to discharge. It has been shown to increase EBP uptake and has been tailored to specific contexts. However, little research has been conducted in East Africa on use of the SCC to improve intrapartum care, particularly for preterm birth despite its burden. We describe checklist adaptation, user acceptability, implementation and lessons learned.

Methods

The East Africa Preterm Birth Initiative (PTBi EA) modified the SCC for use in 23 facilities in Western Kenya and Eastern Uganda as part of a cluster randomized controlled trial evaluating a package of facility-based interventions to improve preterm birth outcomes. The modified SCC (mSCC) for prematurity included: addition of a triage pause point before admission; focus on gestational age assessment, identification and management of preterm labour; and alignment with national guidelines. Following introduction, implementation lasted 24 and 34 months in Uganda and Kenya respectively and was supported through complementary mentoring and data strengthening at all sites. PRONTO® simulation training and quality improvement (QI) activities further supported mSCC use at intervention facilities only. A mixed methods approach, including checklist monitoring, provider surveys and in-depth interviews, was used in this analysis.

Results

A total of 19,443 and 2229 checklists were assessed in Kenya and Uganda, respectively. In both countries, triage and admission pause points had the highest rates of completion. Kenya's completion was greater than 70% for all pause points; Uganda ranged from 39 to 75%. Intervention facilities exposed to PRONTO and QI had higher completion rates than control sites. Provider perceptions cited clinical utility of the checklist, particularly when integrated into patient charts. However, some felt it repeated information in other documentation tools. Completion was hindered by workload and staffing issues.

Conclusion

This study highlights the feasibility and importance of adaptation, iterative modification and complementary activities to reinforce SCC use. There are important opportunities to improve its clinical utility by the addition of prompts specific to the needs of different contexts. The trial assessing the PTBi EA intervention package was registered at ClinicalTrials.gov NCT03112018 Registered December 2016, retrospectively registered.

Background

Household food insecurity (FI) and water insecurity (WI) are prevalent public health issues that can co-occur. Few studies have concurrently assessed their associations with health outcomes, particularly among people living with HIV.

Objectives

We aimed to investigate the associations between FI and WI and how they relate to physical and mental health.

Methods

Food-insecure adult smallholder farmers living with HIV in western Kenya were recruited to participate in a cluster-randomized controlled trial of a multisectoral agricultural and asset loan intervention. We used baseline data on experiences of FI (using the Household Food Insecurity Access Scale, range: 0-27) and WI (using a modified scale developed for this region, range: 0-51) in the prior month (n = 716). Outcomes included probable depression (using the Hopkins Symptom Checklist), fatigue and diarrhea in the prior month, and overall mental and physical health (using the Medical Outcomes Study HIV Health Survey, range: 0-100). We first assessed Pearson correlations between FI, WI, and sociodemographic characteristics. We then developed 3 regressions for each health outcome (control variables and FI; control variables and WI; control variables, FI, and WI) and compared model fit indexes.

Results

Correlations between household FI, WI, and wealth were low, meaning they measure distinct constructs. FI and WI were associated with numerous physical and mental health outcomes; accounting for both resource insecurities typically provided the best model fit. For instance, when controlling for FI, each 10-point higher WI score was associated with a 6.42-point lower physical health score (P < 0.001) and 2.92 times greater odds of probable depression (P < 0.001).

Conclusions

Assessing both FI and WI is important for correctly estimating their relation with health outcomes. Interventions that address food- and water-related issues among persons living with HIV concurrently will likely be more effective at improving health than those addressing a single resource insecurity. This trial was registered at clinicaltrials.gov as NCT02815579.

Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinics. Despite advances in the design and analysis of CRTs, several challenges remain. First, there are many possible ways to specify the causal effect of interest (e.g., at the individual-level or at the cluster-level). Second, the theoretical and practical performance of common methods for CRT analysis remain poorly understood. Here, we present a general framework to formally define an array of causal effects in terms of summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of CRT estimators, including the t-test, generalized estimating equations (GEE), augmented-GEE, and targeted maximum likelihood estimation (TMLE). Using finite sample simulations, we illustrate the practical performance of these estimators for different causal effects and when, as commonly occurs, there are limited numbers of clusters of different sizes. Finally, our application to data from the Preterm Birth Initiative (PTBi) study demonstrates the real-world impact of varying cluster sizes and targeting effects at the cluster-level or at the individual-level. Specifically, the relative effect of the PTBI intervention was 0.81 at the cluster-level, corresponding to a 19% reduction in outcome incidence, and was 0.66 at the individual-level, corresponding to a 34% reduction in outcome risk. Given its flexibility to estimate a variety of user-specified effects and ability to adaptively adjust for covariates for precision gains while maintaining Type-I error control, we conclude TMLE is a promising tool for CRT analysis.

Background

Globally, there were 2.7 million neonatal deaths in 2015. Significant mortality reduction could be achieved by improving care in low- and middle-income countries (LMIC), where the majority of deaths occur. Determining the physical readiness of facilities to identify and manage complications is an essential component of strategies to reduce neonatal mortality.

Methods

We developed clinical cascades for 6 common neonatal conditions then utilized these to assess 23 health facilities in Kenya and Uganda at 2 time-points in 2016 and 2017. We calculated changes in resource availability over time by facility using McNemar's test. We estimated mean readiness and loss of readiness for the 6 conditions and 3 stages of care (identification, treatment, monitoring-modifying treatment). We estimated overall mean readiness and readiness loss across all conditions and stages. Finally, we compared readiness of facilities with a newborn special care unit (NSCU) to those without using the two-sample test of proportions.

Results

The cascade model estimated mean readiness of 26.3-26.6% across the 3 stages for all conditions. Mean readiness ranged from 11.6% (respiratory distress-apnea) to 47.8% (essential newborn care) across both time-points. The model estimated overall mean readiness loss of 30.4-31.9%. There was mild to moderate variability in the timing of readiness loss, with the majority occurring in the identification stage. Overall mean readiness was higher among facilities with a NSCU (36.8%) compared to those without (20.0%).

Conclusion

The cascade model provides a novel approach to quantitatively assess physical readiness for neonatal care. Among 23 facilities in Kenya and Uganda, we identified a consistent pattern of 30-32% readiness loss across cascades and stages. This aggregate measure could be used to monitor and compare readiness at the facility-, health system-, or national-level. Estimates of readiness and loss of readiness may help guide strategies to improve care, prioritize resources, and promote neonatal survival in LMICs.

Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinics. Despite advances in the design and analysis of CRTs, several challenges remain. First, there are many possible ways to specify the causal effect of interest (eg, at the individual-level or at the cluster-level). Second, the theoretical and practical performance of common methods for CRT analysis remain poorly understood. Here, we present a general framework to formally define an array of causal effects in terms of summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of CRT estimators, including the t-test, generalized estimating equations (GEE), augmented-GEE, and targeted maximum likelihood estimation (TMLE). Using finite sample simulations, we illustrate the practical performance of these estimators for different causal effects and when, as commonly occurs, there are limited numbers of clusters of different sizes. Finally, our application to data from the Preterm Birth Initiative (PTBi) study demonstrates the real-world impact of varying cluster sizes and targeting effects at the cluster-level or at the individual-level. Specifically, the relative effect of the PTBi intervention was 0.81 at the cluster-level, corresponding to a 19% reduction in outcome incidence, and was 0.66 at the individual-level, corresponding to a 34% reduction in outcome risk. Given its flexibility to estimate a variety of user-specified effects and ability to adaptively adjust for covariates for precision gains while maintaining Type-I error control, we conclude TMLE is a promising tool for CRT analysis.